In addition to person search, we evaluate the proposed method in a more challenging setting, i.e., character search (joint character detection and identification) in movies [1]}, where context also plays a critical role. We study character search based on a newly-proposed large-scale movie dataset, i.e., MovieNet [1]}. This dataset is more diverse and realistic than existing person search datasets which mostly focus on surveillance scenarios. In character search, the same character may wear different clothes in distinct scenes, bringing significant difficulty to existing person search approaches. In particular, since [1]} only introduces the separate settings of character detection and identification, we re-organize the data to evaluate this new setting, which we refer to as MovieNet-CS. We follow CUHK-SYSU [4]} to provide multiple difficulty levels for character search by varying the number of images in the training/test set on MovieNet-CS. From the subsequent experimental results, we find that our approach notably outperforms the state-of-the-art methods by large margins.
